{"ID":2850950,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.20165","arxiv_id":"2510.20165","title":"IB-GAN: Disentangled Representation Learning with Information Bottleneck Generative Adversarial Networks","abstract":"We propose a new GAN-based unsupervised model for disentangled representation learning. The new model is discovered in an attempt to utilize the Information Bottleneck (IB) framework to the optimization of GAN, thereby named IB-GAN. The architecture of IB-GAN is partially similar to that of InfoGAN but has a critical difference; an intermediate layer of the generator is leveraged to constrain the mutual information between the input and the generated output. The intermediate stochastic layer can serve as a learnable latent distribution that is trained with the generator jointly in an end-to-end fashion. As a result, the generator of IB-GAN can harness the latent space in a disentangled and interpretable manner. With the experiments on dSprites and Color-dSprites dataset, we demonstrate that IB-GAN achieves competitive disentanglement scores to those of state-of-the-art \\b{eta}-VAEs and outperforms InfoGAN. Moreover, the visual quality and the diversity of samples generated by IB-GAN are often better than those by \\b{eta}-VAEs and Info-GAN in terms of FID score on CelebA and 3D Chairs dataset.","short_abstract":"We propose a new GAN-based unsupervised model for disentangled representation learning. The new model is discovered in an attempt to utilize the Information Bottleneck (IB) framework to the optimization of GAN, thereby named IB-GAN. The architecture of IB-GAN is partially similar to that of InfoGAN but has a critical d...","url_abs":"https://arxiv.org/abs/2510.20165","url_pdf":"https://arxiv.org/pdf/2510.20165v1","authors":"[\"Insu Jeon\",\"Wonkwang Lee\",\"Myeongjang Pyeon\",\"Gunhee Kim\"]","published":"2025-10-23T03:24:48Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[\"Generative Adversarial Network\",\"Variational Autoencoder\"]","has_code":false}